window.ai
private-gpt
window.ai | private-gpt | |
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7 | 131 | |
719 | 52,175 | |
- | 3.2% | |
8.8 | 9.2 | |
2 months ago | 11 days ago | |
TypeScript | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
window.ai
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Show HN: I built a free in-browser Llama 3 chatbot powered by WebGPU
Window AI (https://windowai.io/) is an attempt to do something like this with a browser extension.
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GPT-Migrate converts repos from one lang/framework to another
totally agreed that diff projects need to be careful w/ sharing proprietary code w/ third parties.
openai's official stance is that it will never use API calls as training data, and that in my understanding it may retain API call data for up to 30 days for compliance purposes, but that it legally won't store it beyond that (whereas chatgpt convos are meant to be stored and used for training purposes).
as a next step, they could provide a swappable version of the LLM provider using something like https://github.com/imartinez/privateGPT, https://github.com/alexanderatallah/window.ai, etc. would love to have a standard develop here as the community matures around LLM usage
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Releasing local.ai - an LLM local playground with minimal setup
You can actually try out a version of your idea with the sample chat app on windowai.io (will need to grab the extension, the app and the extension work together). In `window.ai` extension, you will want to set the model as `local`, and all call will be routed to `local.ai` for inferencing! <- This enables user of window to test out AI app without incurring any cloud cost :D
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Meet Window AI: A New Way To Use Your Own AI Models On The Web – Including Local Ones
Web: https://windowai.io/
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ChatGPT for Therapy
To make it even more privacy friendly, why not use https://windowai.io to let the user choose which language model to use?
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Isn’t API Unsustainably Expensive?
+1 With Window, the user brings their own AI model and plugs it into the app, so the cost is on the user side, while also giving privacy benefits to the user and removing liabilities for the dev. https://windowai.io/
- Window: Use your own AI models on the web
private-gpt
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
PrivateGPT is a nice tool for this. It's not exactly what you're asking for, but it gets part of the way there.
https://github.com/zylon-ai/private-gpt
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PrivateGPT exploring the Documentation
Further details available at: https://docs.privategpt.dev/api-reference/api-reference/ingestion
- Show HN: I made an app to use local AI as daily driver
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privateGPT VS quivr - a user suggested alternative
2 projects | 12 Jan 2024
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Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
Run https://github.com/imartinez/privateGPT
Then
make ingest /path/to/folder/with/files
Then chat to the LLM.
Done.
Docs: https://docs.privategpt.dev/overview/welcome/quickstart
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Mozilla "MemoryCache" Local AI
PrivateGPT repository in case anyone's interested: https://github.com/imartinez/privateGPT . It doesn't seem to be linked from their official website.
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What Is Retrieval-Augmented Generation a.k.a. RAG
I’m preparing a small internal tool for my work to search documents and provide answers (with references), I’m thinking of using GPT4All [0], Danswer [1] and/or privateGPT [2].
The RAG technique is very close to what I have in mind, but I don’t want the LLM to “hallucinate” and generate answers on its own by synthesizing the source documents. As stated by many others, we’re living in interesting times.
[0] https://gpt4all.io/index.html
[1] https://www.danswer.ai/
[2] https://github.com/imartinez/privateGPT
- LM Studio – Discover, download, and run local LLMs
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Ask HN: Local LLM Recommendation?
https://www.reddit.com/r/LocalLLaMA/comments/14niv66/using_a...
https://github.com/imartinez/privateGPT
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Run ChatGPT-like LLMs on your laptop in 3 lines of code
I've been playing around with https://github.com/imartinez/privateGPT and https://github.com/simonw/llm and wanted to create a simple Python package that made it easier to run ChatGPT-like LLMs on your own machine, use them with non-public data, and integrate them into practical applications.
This resulted in Python package I call OnPrem.LLM.
In the documentation, there are examples for how to use it for information extraction, text generation, retrieval-augmented generation (i.e., chatting with documents on your computer), and text-to-code generation: https://amaiya.github.io/onprem/
Enjoy!
What are some alternatives?
gpt-migrate - Easily migrate your codebase from one framework or language to another.
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
local.ai - 🎒 local.ai - Run AI locally on your PC!
gpt4all - gpt4all: run open-source LLMs anywhere
h2ogpt - Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
llama.cpp - LLM inference in C/C++
LocalAI - :robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.
llama_index - LlamaIndex is a data framework for your LLM applications
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
langchain - 🦜🔗 Build context-aware reasoning applications